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Carbon price forecasting based on CEEMDAN and LSTM

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  • Zhou, Feite
  • Huang, Zhehao
  • Zhang, Changhong

Abstract

After signing the Paris Agreement and piloting carbon trading for many years, China has taken a significant step toward carbon neutrality. Carbon price forecasting is helpful to construct an effective and stable carbon pricing mechanism and provide practical guidance for production, operation, and investment. This paper builds multiple one-step-ahead predictors to analyze and forecast carbon prices, based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory (LSTM) recurrent neural network, with the close prices of Guangzhou Emission Trading Scheme (Guangzhou ETS) from March 14, 2014, to August 31, 2021. After an initial period of unreasonable pricing and market adapting, the carbon price of the Guangzhou ETS began to recover gradually. According to previous literature, this paper summarizes two fundamental CEEMDAN–LSTM frameworks and proposes a hybrid one combined with Variational Modal Decomposition (VMD). With the help of adaptive reducing learning rate Adam optimizer and the early stop mechanism, the forecast turns out stable and reliable results, with a best average coefficient of determination (R2) of 0.982 and Mean Absolute Percentage Error (MAPE) of 0.555%, which shows that Sample Entropy integration and re-decomposition methods are conducive to carbon price forecasting. Validations of four ETS and different timesteps also verify the effectiveness of the hybrid VMD LSTM method, but it still needs to be optimized for practice.

Suggested Citation

  • Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:appene:v:311:y:2022:i:c:s0306261922000782
    DOI: 10.1016/j.apenergy.2022.118601
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    as
    1. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    2. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    3. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    4. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-280, July.
    5. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    6. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    7. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
    8. Frank Convery, 2009. "Origins and Development of the EU ETS," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 43(3), pages 391-412, July.
    9. Bangzhu Zhu & Ping Wang & Julien Chevallier & Yiming Wei, 2015. "Carbon Price Analysis Using Empirical Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 195-206, February.
    10. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    11. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    12. Lo, Alex Y & Mai, Lindsay Qianqing & Lee, Anna Ka-yin & Francesch-Huidobro, Maria & Pei, Qing & Cong, Ren & Chen, Kang, 2018. "Towards network governance? The case of emission trading in Guangdong, China," Land Use Policy, Elsevier, vol. 75(C), pages 538-548.
    13. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    14. Easwaran Narassimhan & Kelly S. Gallagher & Stefan Koester & Julio Rivera Alejo, 2018. "Carbon pricing in practice: a review of existing emissions trading systems," Climate Policy, Taylor & Francis Journals, vol. 18(8), pages 967-991, September.
    15. Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, vol. 5(2), pages 1-16, February.
    16. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
    17. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    18. Boyce, James K., 2018. "Carbon Pricing: Effectiveness and Equity," Ecological Economics, Elsevier, vol. 150(C), pages 52-61.
    19. repec:dau:papers:123456789/4598 is not listed on IDEAS
    20. Cui, Lianbiao & Li, Rongjing & Song, Malin & Zhu, Lei, 2019. "Can China achieve its 2030 energy development targets by fulfilling carbon intensity reduction commitments?," Energy Economics, Elsevier, vol. 83(C), pages 61-73.
    21. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
    22. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    23. Zhang, Kefei & Cao, Hua & Thé, Jesse & Yu, Hesheng, 2022. "A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms," Applied Energy, Elsevier, vol. 306(PA).
    24. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    25. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    26. Chang-Jing Ji & Yu-Jie Hu & Bao-Jun Tang, 2018. "Research on carbon market price mechanism and influencing factors: a literature review," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 761-782, June.
    27. Michael Wara, 2007. "Is the global carbon market working?," Nature, Nature, vol. 445(7128), pages 595-596, February.
    28. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    Full references (including those not matched with items on IDEAS)

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